Experimental Slicing Algorithm Results for 3D Printed Models

Experimental data compares three slicing algorithms for additive manufacturing: equal thickness, adaptive based on triangular facet normals, and a novel adaptive hierarchical method. Results include surface roughness measurements from models printed on a six-degree-of-freedom AM device. The dataset was created by Xingguo Han and published on figshare in 2026.

Use Cases

• Compare surface roughness results from the equal thickness, normal vector-based, and proposed hierarchical slicing algorithms.
• Analyze the relationship between model part classification (non-significant, general, complex feature) and chosen slicing criteria.
• Validate the proposed algorithm's performance on specific test models like the ship, vase, and dice.
• Benchmark printing efficiency metrics against printing accuracy for different slicing methods.

Strengths

• Direct experimental results from a six-degree-of-freedom AM device and roughness meter.
• Compares three distinct algorithmic approaches on multiple 3D models.
• Published under a permissive CC-BY-4.0 license for reuse.

Limitations

• Dataset size is only 5.5 KB, indicating very limited scope and likely a small number of data points.
• Specific column names, row counts, and raw measurement values are not provided in the metadata.
• The temporal and geographic context for the experiments is unknown.

Provenance

Source

Xingguo Han via figshare.

Collection Method

Experimental results from 3D printing tests using a custom six-degree-of-freedom additive manufacturing device.

Freshness

Last updated in 2026, indicating it is a forward-dated or planned publication.